Adaptive information retrieval system based on fuzzy profiling

Alhabashneh, Obada; Iqbal, Rahat; Doctor, Faiyaz; Amin, Saad

doi:10.1109/fuzz-ieee.2015.7338012

Cited by 7 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[3].Medical modeling in many cases deals with uncertainty and depend on many factors that are unmeasured that lead to low accuracy in diagnosis, in the first research was deal with uncertainty and solve a different problem which was introduced by Zadeh as a methodology for computing with words in order to handle uncertainty [4]. Fuzzy applications have a virtually exploded, affecting things we use every day, they use fuzzy sets to construct fuzzy query languages for purposes of information retrieval [5], and other researchers use linguistic hedge such as very, more or less, much, essentially, slightly called a hedge operator [6] [7]. this intelligent gap can understand by computer systems if they use Fuzzy logic in a better way by handling Uncertainty.…”

Section: Related Workmentioning

confidence: 99%

A Proposed Fuzzy Model for Diseases Diagnosis

Alkholy¹,

Aboutabl²,

Haggag³

2020

IJEAT

View full text Add to dashboard Cite

Decision making has become a problem in environments full of uncertain, vague and imprecise information. They face many problems to train computer systems to simulate human thinking to make the right decision. Different methodologies and approaches have been used to train computers to understand and mimic human thinking. This paper proposes a fuzzy model for a bone disease to have the right diagnosis answer, as a human expertise doctor. and to prove that using fuzzy logic has a significant ability to mimic human thinking. The model accepts inputs in the different forms as physiological and clinical parameters and all data based on medical expertise, using a rule-based fuzzy system approach applied with fourteen rules to have final accurate output decisions. it has been tested in the orthopedic unit against the real existing diagnosis answer from expertise doctor and found that is capable of assisting medical experts in diagnosing diseases and provide good health services to their patients.

show abstract

Section: Related Workmentioning

confidence: 99%

A Proposed Fuzzy Model for Diseases Diagnosis

Alkholy¹,

Aboutabl²,

Haggag³

2020

IJEAT

View full text Add to dashboard Cite

show abstract

“…For example, for purchase transactions, we have the available amounts, items, and time, but not the age or sex of the client. Properties may be fuzzy retrieved within Big Data, such as for fuzzy information retrieval systems (see, for instance, [58]). The searching of profiles should be viewed as questions to Big Data-questions that are, however, formulated with the kinds of data available and are only partially chosen by the researcher.…”

Section: Non-computable Profiles: Emergent Profilesmentioning

confidence: 99%

Big Data: From Forecasting to Mesoscopic Understanding. Meta-Profiling as Complex Systems

Minati¹

2019

Systems

View full text Add to dashboard Cite

We consider Big Data as a phenomenon with acquired properties, similar to collective behaviours, that establishes virtual collective beings. We consider the occurrence of ongoing non-equivalent multiple properties in the conceptual framework of structural dynamics given by sequences of structures and not only by different values assumed by the same structure. We consider the difference between modelling and profiling in a constructivist way, as De Finetti intended probability to exist, depending on the configuration taken into consideration. The past has little or no influence, while events and their configurations are not memorised. Any configuration of events is new, and the probabilistic values to be considered are reset. As for collective behaviours, we introduce methodological and conceptual proposals using mesoscopic variables and their property profiles and meta-profile Big Data and non-computable profiles which were inspired by the use of natural computing to deal with cyber-ecosystems. The focus is on ongoing profiles, in which the arising properties trace trajectories, rather than assuming that we can foresee them based on the past.We assume that dealing with Big Data as a phenomenon contains not only traces of past events, but also the assumption of having properties that are independent from the original event, when studied as a virtual collective being [6]. Thus, we consider the occurrence of ongoing, non-equivalent, multiple properties with any initial temporal beginning, duration, and variable combination in conceptual correspondence with the nature of quasi-systems, which occur when a system is not always the same system and or even a system at all [7,8].We then mention the criticality of the level of description, as well as the scalarity assumed to detect the properties as well as the conceptual ineffectiveness of increasing the quantity of data available. Before proposing new approaches allowed by the conceptual 'definitions' mentioned above, we consider the differences between models and profiles. Models (especially ideal) are expected to support understanding, while profiling is intended to represent non-ideal, data-driven models, and their emergent, ongoing properties, for example, correlations and coherences [9]. Another differentiation considered is between forecasting and understanding. Forecasting is based on the importance of the past, is based on analogies and repetitiveness, and takes some contextual conditions into account. Understanding is considered to be the ability to conjecture, hypothesise, speculate, and, finally, realise the nature of the phenomenon under study, for instance, chaotic, hosting bifurcations, fluctuations, and the presence of multiple dynamic coherences.We specify that the differences introduced above should be considered in a constructivist conceptual framework in which, instead of trying to find how a phenomenon 'really' is, one looks for the most effective way to think of it (multiple modelling) [10]. For instance, we consider the conceptual framework of the t...

show abstract

“…According to Pacheco-Ryes et al (2015), Alhabashneh et al (2015) and Burgos et al (2006) AdaptiveInformationRetrieval(AIR)worksbyretrievinginformationthatisrelevanttotheuser request.Pacheco-Ryesetal. (2015)doesindicatethatAIRdoesvarydependingonthedesignofthe system,rangingfromprovidingeffectiveretrievalofbooks,journals,textualandvisualinformation.…”

Section: Adaptive Information Retrievalmentioning

confidence: 99%

End User Perspective of e-Learning using LMS-Like Systems

Costello

2017

International Journal of People-Oriented Programming

View full text Add to dashboard Cite

In literature, e-learning plays an important part in improving the needs of learners and educators, as well as other stakeholders and institutions. However, this research indicates that e-learning is often used as a repository for uploading academic materials, without taking into consideration characteristics of the learner. This investigation examines a variety of techniques adopted from e-learning, adaptive learning and User Modelling to suggest improvements within industry. Throughout the paper, there will be a strong influence on how future PLEs should be designed and tailored to challenge the end users' perception of on-line education to meet the future needs of the learner, the educator and the institution. Focusing on future trends would allow developers to encapsulate learners motivational and learning needs. These aspects would enable designers to improve usability, functionality and reliability. PLEs are not just a collection of applications tailored towards personalisation, they are significantly more important than that. These systems are designed to be a functioned application/platform/management-system that promotes and encourages Personalised Learning. The research found within the case studies have shown that concepts of PLEs have contributed towards the learners' experience.

show abstract

Adaptive information retrieval system based on fuzzy profiling

Cited by 7 publications

References 36 publications

A Proposed Fuzzy Model for Diseases Diagnosis

A Proposed Fuzzy Model for Diseases Diagnosis

Big Data: From Forecasting to Mesoscopic Understanding. Meta-Profiling as Complex Systems

End User Perspective of e-Learning using LMS-Like Systems

Contact Info

Product

Resources

About